This data series contains 540 temporal datasets. Wildfire adheres to meteorological enablers and drivers across a spectrum of timescales. However, a majority of downscaling methods are ill suited for wildfire application due the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias-Corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA), that directly incorporate daily data were validated over the Western United States with reanalysis data. While both methods outperformed the null interpolation only method, MACA exhibited additional skill in temperature, humidity, wind and precipitation due to its ability to jointly downscale temperature and dew point temperature and its use of analog patterns rather than interpolation. Both downscaling methods exhibited value added information in tracking fire danger indices and periods of extreme fire danger; however, due to its ability to more accurately capture relative humidity and winds, MACA outperformed the daily BCSD.
